setwd("D:/BINUS/Semester_4/Group 2_Indonesia Visitor Analysis/Group 2_Indonesia Visitor Analysis")
data <- read.csv("visitor_asean.csv")
#data overview
str(data)
## 'data.frame': 907 obs. of 8 variables:
## $ Destination.Country: chr "Brunei Darussalam" "Brunei Darussalam" "Brunei Darussalam" "Brunei Darussalam" ...
## $ Origin.Country : chr "Total Intra-ASEAN" "Total Country (World)" "Total EU-27" "Total EU-28" ...
## $ X2015 : num 118719 218213 7750 18879 9972 ...
## $ X2016 : num 117525 218809 6941 17301 7989 ...
## $ X2017 : num 131351 258955 7581 19673 8600 ...
## $ X2018 : num 134596 278136 7338 19304 9702 ...
## $ X2019 : num 168705 333244 5884 20082 10188 ...
## $ X2020 : num 32571 62325 2034 NaN 2597 ...
head(data)
## Destination.Country Origin.Country X2015 X2016 X2017 X2018 X2019
## 1 Brunei Darussalam Total Intra-ASEAN 118719 117525 131351 134596 168705
## 2 Brunei Darussalam Total Country (World) 218213 218809 258955 278136 333244
## 3 Brunei Darussalam Total EU-27 7750 6941 7581 7338 5884
## 4 Brunei Darussalam Total EU-28 18879 17301 19673 19304 20082
## 5 Brunei Darussalam Australia [AU] 9972 7989 8600 9702 10188
## 6 Brunei Darussalam Austria [AT] 122 128 101 158 NaN
## X2020
## 1 32571
## 2 62325
## 3 2034
## 4 NaN
## 5 2597
## 6 18
#missing value
missing_values <- sapply(data, function(x) sum(is.na(x)))
print("Missing values in each feature:")
## [1] "Missing values in each feature:"
print(missing_values)
## Destination.Country Origin.Country X2015 X2016
## 0 0 210 383
## X2017 X2018 X2019 X2020
## 55 85 97 75
#data imputation
data_impute <- na.omit(data)
print("Data Table after dropping rows with missing values:")
## [1] "Data Table after dropping rows with missing values:"
print(data_impute)
## Destination.Country Origin.Country X2015 X2016
## 1 Brunei Darussalam Total Intra-ASEAN 118719 117525
## 2 Brunei Darussalam Total Country (World) 218213 218809
## 3 Brunei Darussalam Total EU-27 7750 6941
## 5 Brunei Darussalam Australia [AU] 9972 7989
## 8 Brunei Darussalam Bangladesh [BD] 1531 2627
## 10 Brunei Darussalam Cambodia [KH] 251 318
## 11 Brunei Darussalam Canada [CA] 2245 2013
## 12 Brunei Darussalam China [CN] 36886 40838
## 14 Brunei Darussalam Denmark [DK] 250 272
## 17 Brunei Darussalam France [FR] 1700 1340
## 18 Brunei Darussalam Germany [DE] 1923 1657
## 21 Brunei Darussalam India [IN] 6379 7193
## 22 Brunei Darussalam Indonesia [ID] 17152 20844
## 23 Brunei Darussalam Ireland [IE] 303 301
## 24 Brunei Darussalam Italy [IT] 543 512
## 25 Brunei Darussalam Japan [JP] 4336 4474
## 26 Brunei Darussalam Korea [KR] 2871 3551
## 28 Brunei Darussalam Lao PDR [LA] 125 192
## 29 Brunei Darussalam Malaysia [MY] 57986 54374
## 30 Brunei Darussalam Myanmar [MM] 618 596
## 31 Brunei Darussalam Nepal [NP] 1092 1129
## 32 Brunei Darussalam Netherlands [NL] 1348 1020
## 33 Brunei Darussalam New Zealand [NZ] 1641 1454
## 34 Brunei Darussalam Norway [NO] 348 229
## 35 Brunei Darussalam Oman [OM] 366 267
## 36 Brunei Darussalam Others Unspecified Countries [O0] 3435 3273
## 37 Brunei Darussalam Pakistan [PK] 621 656
## 38 Brunei Darussalam Philippines [PH] 17922 17064
## 44 Brunei Darussalam Singapore [SG] 16230 14473
## 46 Brunei Darussalam Sri Lanka [LK] 352 291
## 47 Brunei Darussalam Sweden [SE] 292 262
## 50 Brunei Darussalam Thailand [TH] 5831 6303
## 53 Brunei Darussalam United Kingdom [GB] 11129 10360
## 54 Brunei Darussalam United States [US] 3704 3408
## 55 Brunei Darussalam Viet Nam [VN] 2604 3361
## 56 Cambodia Total Intra-ASEAN 2097758 2121220
## 57 Cambodia Total Country (World) 4775231 5011712
## 58 Cambodia Total EU-27 456880 458431
## 60 Cambodia Afghanistan [AF] 121 158
## 61 Cambodia Argentina [AR] 7870 13497
## 62 Cambodia Australia [AU] 134748 146806
## 63 Cambodia Austria [AT] 10080 11203
## 64 Cambodia Bangladesh [BD] 2057 1851
## 65 Cambodia Belgium [BE] 17452 18047
## 66 Cambodia Bhutan [BT] 229 226
## 67 Cambodia Brazil [BR] 10618 12526
## 68 Cambodia Brunei Darussalam [BN] 852 1120
## 69 Cambodia Bulgaria [BG] 1771 1837
## 70 Cambodia Cameroon [CM] 202 120
## 71 Cambodia Canada [CA] 56834 60715
## 72 Cambodia Chile [CL] 4968 6208
## 73 Cambodia China [CN] 694712 830003
## 74 Cambodia Colombia [CO] 2903 3510
## 75 Cambodia Costa Rica [CR] 465 581
## 76 Cambodia Croatia [HR] 1304 1213
## 77 Cambodia Czech Republic [CZ] 5603 5761
## 78 Cambodia Denmark [DK] 11553 11462
## 79 Cambodia Ecuador [EC] 560 612
## 80 Cambodia Egypt [EG] 587 696
## 81 Cambodia Estonia [EE] 1952 1889
## 82 Cambodia Fiji [FJ] 191 230
## 83 Cambodia Finland [FI] 7246 6639
## 84 Cambodia France [FR] 145724 150294
## 85 Cambodia Germany [DE] 94040 108784
## 86 Cambodia Ghana [GH] 93 190
## 87 Cambodia Greece [GR] 1595 1652
## 88 Cambodia Honduras [HN] 112 191
## 89 Cambodia Hong Kong SAR [HK] 14787 15980
## 90 Cambodia Hungary [HU] 4190 4682
## 91 Cambodia Iceland [IS] 727 815
## 92 Cambodia India [IN] 36671 46131
## 93 Cambodia Indonesia [ID] 43147 48771
## 94 Cambodia Iraq [IQ] 169 203
## 95 Cambodia Ireland [IE] 11200 11943
## 96 Cambodia Islamic Republic of Iran [IR] 525 600
## 97 Cambodia Israel [IL] 9557 11180
## 98 Cambodia Italy [IT] 32177 35794
## 99 Cambodia Jamaica [JM] 143 108
## 100 Cambodia Japan [JP] 193330 191577
## 101 Cambodia Jordan [JO] 340 1030
## 102 Cambodia Kazakhstan [KZ] 2235 1772
## 103 Cambodia Kenya [KE] 344 417
## 104 Cambodia Korea [KR] 395259 357194
## 105 Cambodia Kuwait [KW] 916 728
## 106 Cambodia Kyrgyz Republic [KG] 161 175
## 107 Cambodia Lao PDR [LA] 405359 369335
## 108 Cambodia Latvia [LV] 1081 1023
## 109 Cambodia Liberia [LR] 55 46
## 110 Cambodia Lithuania [LT] 1861 2211
## 111 Cambodia Luxembourg [LU] 690 859
## 112 Cambodia Macao SAR [MO] 392 404
## 113 Cambodia Malaysia [MY] 149389 152843
## 114 Cambodia Malta [MT] 462 512
## 115 Cambodia Mexico [MX] 7128 8301
## 116 Cambodia Monaco [MC] 89 78
## 117 Cambodia Mongolia [MN] 458 478
## 118 Cambodia Morocco [MA] 505 509
## 119 Cambodia Myanmar [MM] 8965 12819
## 120 Cambodia Nepal [NP] 2358 2505
## 121 Cambodia Netherlands [NL] 29015 30885
## 122 Cambodia New Zealand [NZ] 23021 24797
## 123 Cambodia Nigeria [NG] 1069 1507
## 124 Cambodia Norway [NO] 9521 9666
## 125 Cambodia Occupied Palestinian Territory [PS] 84 103
## 126 Cambodia Oman [OM] 214 212
## 127 Cambodia Others Unspecified Countries [O0] 18037 65755
## 128 Cambodia Pakistan [PK] 1521 1627
## 129 Cambodia Peru [PE] 1060 1679
## 130 Cambodia Philippines [PH] 84677 108032
## 131 Cambodia Poland [PL] 14008 15912
## 132 Cambodia Portugal [PT] 5947 7526
## 133 Cambodia Qatar [QA] 117 87
## 134 Cambodia Republic of Congo [CG] 107 110
## 135 Cambodia Romania [RO] 2414 2620
## 136 Cambodia Russia [RU] 55500 53164
## 137 Cambodia Saudi Arabia [SA] 307 349
## 138 Cambodia Serbia and Montenegro [ZB] 863 1052
## 139 Cambodia Singapore [SG] 67669 70556
## 140 Cambodia Slovak Republic [SK] 2266 2740
## 141 Cambodia Slovenia [SI] 1257 1218
## 142 Cambodia South Africa [ZA] 5336 5743
## 143 Cambodia Spain [ES] 34847 4047
## 144 Cambodia Sri Lanka [LK] 1933 2412
## 145 Cambodia Sudan [SD] 131 123
## 146 Cambodia Sweden [SE] 17145 17678
## 147 Cambodia Switzerland [CH] 21979 26582
## 148 Cambodia Syria [SY] 260 629
## 149 Cambodia Taiwan Province of China [TW] 109727 104765
## 150 Cambodia Thailand [TH] 349908 398081
## 152 Cambodia Tunisia [TN] 207 392
## 153 Cambodia Turkey [TR] 5802 6623
## 154 Cambodia Ukraine [UA] 5322 5414
## 155 Cambodia United Arab Emirates [AE] 367 333
## 156 Cambodia United Kingdom [GB] 154265 159489
## 157 Cambodia United States [US] 217510 238658
## 158 Cambodia Uruguay [UY] 1004 1166
## 159 Cambodia Uzbekistan [UZ] 735 491
## 160 Cambodia Venezuela [VE] 723 469
## 161 Cambodia Viet Nam [VN] 987792 959663
## 162 Cambodia Yemen [YE] 452 313
## 163 Indonesia Total Intra-ASEAN 3860652 3817500
## 164 Indonesia Total Country (World) 10406759 11519275
## 165 Indonesia Total EU-27 883697 1049790
## 178 Indonesia Australia [AU] 1099058 1302292
## 179 Indonesia Austria [AT] 22791 24375
## 181 Indonesia Bahrain [BH] 1687 2243
## 182 Indonesia Bangladesh [BD] 16162 39026
## 184 Indonesia Belgium [BE] 38842 43607
## 192 Indonesia Brunei Darussalam [BN] 18304 23693
## 199 Indonesia Canada [CA] 75816 86804
## 203 Indonesia China [CN] 1260700 1556771
## 215 Indonesia Denmark [DK] 28020 36380
## 220 Indonesia Egypt [EG] 13102 19948
## 227 Indonesia Finland [FI] 19029 21031
## 228 Indonesia France [FR] 212575 256229
## 233 Indonesia Germany [DE] 203611 243873
## 245 Indonesia Hong Kong SAR [HK] 94109 101369
## 248 Indonesia India [IN] 319608 422045
## 254 Indonesia Italy [IT] 69427 79424
## 256 Indonesia Japan [JP] 549705 545392
## 261 Indonesia Korea [KR] 387473 386789
## 262 Indonesia Kuwait [KW] 8320 6368
## 276 Indonesia Malaysia [MY] 1458593 1541197
## 291 Indonesia Myanmar [MM] 40635 44721
## 295 Indonesia Netherlands [NL] 175317 200811
## 297 Indonesia New Zealand [NZ] 87923 105393
## 301 Indonesia Norway [NO] 18842 19478
## 304 Indonesia Others Unspecified Countries [O0] 580132 709585
## 305 Indonesia Pakistan [PK] 7976 10098
## 311 Indonesia Philippines [PH] 273630 298910
## 313 Indonesia Portugal [PT] 22206 29286
## 315 Indonesia Qatar [QA] 1608 1856
## 318 Indonesia Russia [RU] 72707 88520
## 323 Indonesia Saudi Arabia [SA] 164778 197681
## 328 Indonesia Singapore [SG] 1624058 1515699
## 332 Indonesia South Africa [ZA] 23052 29229
## 333 Indonesia Spain [ES] 53891 68840
## 334 Indonesia Sri Lanka [LK] 11496 24256
## 341 Indonesia Sweden [SE] 37988 45934
## 342 Indonesia Switzerland [CH] 52309 56700
## 344 Indonesia Taiwan Province of China [TW] 227912 252849
## 346 Indonesia Thailand [TH] 120879 124569
## 358 Indonesia United Arab Emirates [AE] 10325 9016
## 359 Indonesia United Kingdom [GB] 292745 352017
## 360 Indonesia United States [US] 276027 316782
## 365 Indonesia Viet Nam [VN] 50165 60984
## 367 Indonesia Yemen [YE] 8838 9478
## 368 Lao PDR Total Intra-ASEAN 3588538 3083383
## 369 Lao PDR Total Country (World) 4684429 4239047
## 370 Lao PDR Total EU-27 132369 136579
## 372 Lao PDR Australia [AU] 34665 33077
## 373 Lao PDR Austria [AT] 3910 5324
## 374 Lao PDR Belgium [BE] 5289 5682
## 375 Lao PDR Brunei Darussalam [BN] 865 484
## 376 Lao PDR Cambodia [KH] 20625 16536
## 377 Lao PDR Canada [CA] 19785 19315
## 378 Lao PDR China [CN] 511436 545493
## 379 Lao PDR Denmark [DK] 4491 4479
## 380 Lao PDR Finland [FI] 3013 3218
## 381 Lao PDR France [FR] 55151 54953
## 382 Lao PDR Germany [DE] 31897 34018
## 383 Lao PDR Greece [GR] 878 593
## 384 Lao PDR India [IN] 5492 8249
## 385 Lao PDR Indonesia [ID] 6019 5010
## 386 Lao PDR Israel [IL] 4163 3593
## 387 Lao PDR Italy [IT] 8990 10052
## 388 Lao PDR Japan [JP] 43826 49191
## 389 Lao PDR Korea [KR] 165328 173260
## 390 Lao PDR Malaysia [MY] 24095 24391
## 391 Lao PDR Myanmar [MM] 2661 3695
## 392 Lao PDR Netherlands [NL] 8429 7004
## 393 Lao PDR New Zealand [NZ] 4798 4787
## 394 Lao PDR Norway [NO] 3499 4018
## 395 Lao PDR Others Unspecified Countries [O0] 37524 43197
## 396 Lao PDR Philippines [PH] 16709 16750
## 397 Lao PDR Russia [RU] 12532 13033
## 398 Lao PDR Singapore [SG] 8258 8512
## 399 Lao PDR Spain [ES] 4856 5461
## 400 Lao PDR Sweden [SE] 5465 5795
## 401 Lao PDR Switzerland [CH] 9777 10603
## 402 Lao PDR Taiwan Province of China [TW] 6131 14005
## 403 Lao PDR Thailand [TH] 2321352 2009605
## 404 Lao PDR United Kingdom [GB] 41508 39170
## 405 Lao PDR United States [US] 63058 58094
## 406 Lao PDR Viet Nam [VN] 1187954 998400
## 407 Malaysia Total Intra-ASEAN 19146514 20271144
## 408 Malaysia Total Country (World) 25721251 26757392
## 409 Malaysia Total EU-27 585835 514230
## 412 Malaysia Australia [AU] 486948 377727
## 416 Malaysia Belgium [BE] 18789 14283
## 418 Malaysia Brunei Darussalam [BN] 1133555 1391016
## 419 Malaysia Cambodia [KH] 75059 61844
## 420 Malaysia Canada [CA] 79557 72337
## 421 Malaysia China [CN] 1677163 2124942
## 423 Malaysia Denmark [DK] 24113 21612
## 424 Malaysia Egypt [EG] 25637 30231
## 426 Malaysia France [FR] 151474 134257
## 427 Malaysia Germany [DE] 144910 130276
## 429 Malaysia Indonesia [ID] 2788033 3049964
## 431 Malaysia Ireland [IE] 22746 18208
## 433 Malaysia Italy [IT] 51946 42747
## 434 Malaysia Japan [JP] 483569 413768
## 437 Malaysia Korea [KR] 421161 444439
## 439 Malaysia Lao PDR [LA] 24448 31061
## 444 Malaysia Myanmar [MM] 66553 49175
## 447 Malaysia Netherlands [NL] 84584 72200
## 448 Malaysia New Zealand [NZ] 60846 53352
## 451 Malaysia Norway [NO] 18622 14709
## 453 Malaysia Others Unspecified Countries [O0] 320900 1223374
## 457 Malaysia Philippines [PH] 554917 417446
## 461 Malaysia Russia [RU] 55263 50893
## 462 Malaysia Saudi Arabia [SA] 99754 123878
## 463 Malaysia Singapore [SG] 12930754 13272961
## 464 Malaysia South Africa [ZA] 20625 20053
## 465 Malaysia Spain [ES] 36692 28018
## 468 Malaysia Sweden [SE] 35586 32861
## 469 Malaysia Switzerland [CH] 28141 26628
## 471 Malaysia Taiwan Province of China [TW] 283224 300861
## 472 Malaysia Thailand [TH] 1343569 1780800
## 474 Malaysia Turkey [TR] 15395 13029
## 476 Malaysia United Arab Emirates [AE] 15769 14150
## 477 Malaysia United Kingdom [GB] 401019 400269
## 478 Malaysia United States [US] 237768 217075
## 481 Malaysia Viet Nam [VN] 229626 216877
## 482 Myanmar Total Intra-ASEAN 1762886 402862
## 483 Myanmar Total Country (World) 4681020 2907207
## 484 Myanmar Total EU-27 156453 148672
## 494 Myanmar Australia [AU] 30820 34010
## 495 Myanmar Austria [AT] 4398 4857
## 500 Myanmar Belgium [BE] 6666 7783
## 515 Myanmar Canada [CA] 14051 15024
## 519 Myanmar China [CN] 2102677 183886
## 544 Myanmar France [FR] 47435 52304
## 547 Myanmar Germany [DE] 35727 39044
## 563 Myanmar India [IN] 59692 38537
## 569 Myanmar Italy [IT] 14841 17969
## 571 Myanmar Japan [JP] 90312 100784
## 575 Myanmar Korea [KR] 63715 64397
## 590 Myanmar Malaysia [MY] 40852 43931
## 606 Myanmar Netherlands [NL] 12174 13950
## 607 Myanmar New Zealand [NZ] 4547 5026
## 621 Myanmar Philippines [PH] 19075 16421
## 629 Myanmar Russia [RU] 4138 5487
## 639 Myanmar Singapore [SG] 45125 50198
## 644 Myanmar Spain [ES] 9158 12765
## 653 Myanmar Switzerland [CH] 12293 13897
## 655 Myanmar Taiwan Province of China [TW] 31735 36118
## 657 Myanmar Thailand [TH] 1604212 243443
## 669 Myanmar United Kingdom [GB] 45120 51051
## 670 Myanmar United States [US] 69815 76502
## 675 Myanmar Viet Nam [VN] 31150 48869
## 677 Philippines Total Intra-ASEAN 481567 461698
## 678 Philippines Total Country (World) 5360682 5967005
## 679 Philippines Total EU-27 292723 341423
## 681 Philippines Andorra [AD] 329 364
## 682 Philippines Argentina [AR] 1745 2369
## 683 Philippines Australia [AU] 241187 251098
## 684 Philippines Austria [AT] 12267 13087
## 685 Philippines Bahrain [BH] 3562 3689
## 686 Philippines Bangladesh [BD] 4664 4516
## 687 Philippines Belgium [BE] 12825 14477
## 688 Philippines Brazil [BR] 3495 4520
## 689 Philippines Brunei Darussalam [BN] 9015 8211
## 690 Philippines Cambodia [KH] 3503 3526
## 691 Philippines Canada [CA] 156363 175631
## 692 Philippines China [CN] 490841 675663
## 693 Philippines Colombia [CO] 1228 1431
## 694 Philippines Denmark [DK] 15269 18049
## 695 Philippines Egypt [EG] 1630 1985
## 696 Philippines Finland [FI] 6548 6318
## 697 Philippines France [FR] 45505 55384
## 698 Philippines Germany [DE] 75348 86363
## 699 Philippines Greece [GR] 2386 2483
## 700 Philippines Guam [GU] 35262 38777
## 701 Philippines Hong Kong SAR [HK] 122180 116328
## 702 Philippines India [IN] 74824 90816
## 703 Philippines Indonesia [ID] 48178 44348
## 704 Philippines Ireland [IE] 14050 16557
## 705 Philippines Islamic Republic of Iran [IR] 2505 2720
## 706 Philippines Israel [IL] 11756 16725
## 707 Philippines Italy [IT] 21620 25945
## 708 Philippines Japan [JP] 495662 535238
## 709 Philippines Jordan [JO] 677 718
## 710 Philippines Korea [KR] 1339678 1475081
## 711 Philippines Kuwait [KW] 5440 6649
## 712 Philippines Lao PDR [LA] 1231 1173
## 713 Philippines Luxembourg [LU] 687 833
## 714 Philippines Macao SAR [MO] 10160 9247
## 715 Philippines Malaysia [MY] 155814 139133
## 716 Philippines Mexico [MX] 3145 2924
## 717 Philippines Myanmar [MM] 7033 7442
## 718 Philippines Nauru [NR] 20 17
## 719 Philippines Nepal [NP] 2908 3449
## 720 Philippines Netherlands [NL] 28632 31876
## 721 Philippines New Zealand [NZ] 20579 23431
## 722 Philippines Nigeria [NG] 1430 1489
## 723 Philippines Norway [NO] 20968 21606
## 725 Philippines Others Unspecified Countries [O0] 271037 257991
## 726 Philippines Pakistan [PK] 4346 4256
## 727 Philippines Papua New Guinea [PG] 5910 7738
## 728 Philippines Peru [PE] 561 581
## 729 Philippines Poland [PL] 8030 8893
## 730 Philippines Portugal [PT] 2206 2999
## 731 Philippines Qatar [QA] 4472 4745
## 732 Philippines Russia [RU] 25278 28210
## 733 Philippines Saudi Arabia [SA] 50884 56081
## 734 Philippines Singapore [SG] 181176 176057
## 735 Philippines South Africa [ZA] 4570 5117
## 736 Philippines Spain [ES] 24144 32097
## 737 Philippines Sri Lanka [LK] 5292 5410
## 738 Philippines Sweden [SE] 23206 26062
## 739 Philippines Switzerland [CH] 27200 29420
## 740 Philippines Taiwan Province of China [TW] 177670 229303
## 741 Philippines Thailand [TH] 44038 47913
## 742 Philippines Turkey [TR] 6026 7884
## 743 Philippines United Arab Emirates [AE] 16881 17634
## 744 Philippines United Kingdom [GB] 154589 173299
## 745 Philippines United States [US] 779217 869463
## 746 Philippines Venezuela [VE] 221 271
## 747 Philippines Viet Nam [VN] 31579 33895
## 748 Singapore Total Intra-ASEAN 5748155 5936364
## 749 Singapore Total Country (World) 15231469 16403595
## 750 Singapore Total EU-27 842294 886275
## 752 Singapore Australia [AU] 1043568 1027314
## 754 Singapore Bangladesh [BD] 122587 123343
## 756 Singapore Brunei Darussalam [BN] 73594 69891
## 758 Singapore Canada [CA] 96247 98474
## 759 Singapore China [CN] 2106164 2863669
## 760 Singapore Denmark [DK] 31389 31246
## 761 Singapore Egypt [EG] 4538 4426
## 762 Singapore Finland [FI] 27904 28642
## 763 Singapore France [FR] 157483 170913
## 764 Singapore Germany [DE] 286732 328765
## 766 Singapore Hong Kong SAR [HK] 609888 537970
## 767 Singapore India [IN] 1013986 1097200
## 768 Singapore Indonesia [ID] 2731690 2893646
## 769 Singapore Ireland [IE] 17347 18943
## 770 Singapore Islamic Republic of Iran [IR] 12267 22641
## 771 Singapore Israel [IL] 15743 18635
## 772 Singapore Italy [IT] 69350 74629
## 773 Singapore Japan [JP] 789179 783863
## 774 Singapore Korea [KR] 577082 566510
## 775 Singapore Kuwait [KW] 10767 11123
## 777 Singapore Malaysia [MY] 1171077 1151584
## 778 Singapore Mauritius [MU] 8739 12832
## 779 Singapore Myanmar [MM] 105452 113616
## 781 Singapore Netherlands [NL] 79052 82245
## 782 Singapore New Zealand [NZ] 127618 121100
## 783 Singapore Norway [NO] 31650 29250
## 784 Singapore Others Unspecified Countries [O0] 278200 395117
## 785 Singapore Pakistan [PK] 18948 22213
## 786 Singapore Philippines [PH] 673374 691643
## 788 Singapore Russia [RU] 63842 70404
## 789 Singapore Saudi Arabia [SA] 16091 14520
## 790 Singapore South Africa [ZA] 32470 32834
## 791 Singapore Spain [ES] 48074 49491
## 792 Singapore Sri Lanka [LK] 93052 101905
## 793 Singapore Sweden [SE] 42562 43765
## 794 Singapore Switzerland [CH] 100849 101455
## 795 Singapore Taiwan Province of China [TW] 378026 394216
## 796 Singapore Thailand [TH] 516409 546555
## 798 Singapore United Arab Emirates [AE] 78693 80234
## 799 Singapore United Kingdom [GB] 473810 489224
## 800 Singapore United States [US] 499509 516454
## 801 Singapore Viet Nam [VN] 418266 469429
## 802 Thailand Total Intra-ASEAN 7886136 8897291
## 803 Thailand Total Country (World) 29881091 32529588
## 804 Thailand Total EU-27 2880089 3002656
## 806 Thailand Argentina [AR] 28965 46390
## 807 Thailand Australia [AU] 805946 813017
## 808 Thailand Austria [AT] 97806 97989
## 809 Thailand Bangladesh [BD] 107394 103616
## 810 Thailand Belgium [BE] 106100 112140
## 811 Thailand Brazil [BR] 48522 63139
## 812 Thailand Brunei Darussalam [BN] 13833 17994
## 813 Thailand Cambodia [KH] 487487 684836
## 814 Thailand Canada [CA] 227306 222358
## 815 Thailand China [CN] 7934791 8821148
## 816 Thailand Denmark [DK] 159425 163406
## 817 Thailand Egypt [EG] 25216 24913
## 818 Thailand Finland [FI] 134731 131207
## 819 Thailand France [FR] 681097 697738
## 820 Thailand Germany [DE] 760604 825496
## 821 Thailand Hong Kong SAR [HK] 669165 690465
## 822 Thailand India [IN] 1069149 1076970
## 823 Thailand Indonesia [ID] 469226 558499
## 824 Thailand Israel [IL] 141021 161254
## 825 Thailand Italy [IT] 246066 248903
## 826 Thailand Japan [JP] 1381690 1416903
## 827 Thailand Korea [KR] 1372995 1449617
## 828 Thailand Kuwait [KW] 66772 68168
## 829 Thailand Lao PDR [LA] 1233138 1414916
## 830 Thailand Malaysia [MY] 3423397 3506199
## 831 Thailand Myanmar [MM] 259678 363871
## 832 Thailand Nepal [NP] 32678 43170
## 833 Thailand Netherlands [NL] 221657 228443
## 834 Thailand New Zealand [NZ] 112314 99402
## 835 Thailand Norway [NO] 135347 134335
## 836 Thailand Others Unspecified Countries [O0] 1124238 1308314
## 837 Thailand Pakistan [PK] 78619 71720
## 838 Thailand Philippines [PH] 310975 323860
## 839 Thailand Russia [RU] 884085 1085890
## 840 Thailand Saudi Arabia [SA] 19163 33038
## 841 Thailand Singapore [SG] 937311 1163309
## 843 Thailand Spain [ES] 150940 168264
## 844 Thailand Sri Lanka [LK] 75429 67876
## 845 Thailand Sweden [SE] 321663 329070
## 846 Thailand Switzerland [CH] 206454 226412
## 847 Thailand Taiwan Province of China [TW] 552624 513528
## 848 Thailand United Arab Emirates [AE] 124719 187665
## 849 Thailand United Kingdom [GB] 946919 961471
## 850 Thailand United States [US] 867520 938862
## 851 Thailand Viet Nam [VN] 751091 863807
## 852 Viet Nam Total Intra-ASEAN 1300839 1461172
## 853 Viet Nam Total Country (World) 7943651 10012735
## 854 Viet Nam Total EU-27 598205 701616
## 857 Viet Nam Australia [AU] 303721 320678
## 860 Viet Nam Belgium [BE] 23939 26231
## 863 Viet Nam Cambodia [KH] 227074 211949
## 864 Viet Nam Canada [CA] 105670 122929
## 865 Viet Nam China [CN] 1780918 2696848
## 868 Viet Nam Denmark [DK] 28293 30996
## 869 Viet Nam Finland [FI] 15043 15953
## 870 Viet Nam France [FR] 211636 240808
## 871 Viet Nam Germany [DE] 149079 176015
## 874 Viet Nam Indonesia [ID] 62240 69653
## 877 Viet Nam Italy [IT] 40291 51265
## 878 Viet Nam Japan [JP] 671379 740592
## 880 Viet Nam Korea [KR] 1112978 1543883
## 881 Viet Nam Lao PDR [LA] 113992 137004
## 883 Viet Nam Malaysia [MY] 346584 407574
## 887 Viet Nam Netherlands [NL] 52967 64712
## 888 Viet Nam New Zealand [NZ] 31960 42588
## 889 Viet Nam Norway [NO] 21425 23110
## 890 Viet Nam Others Unspecified Countries [O0] 299075 544458
## 892 Viet Nam Philippines [PH] 99757 110967
## 894 Viet Nam Russia [RU] 338843 433987
## 896 Viet Nam Singapore [SG] 236547 257041
## 897 Viet Nam Spain [ES] 44932 57957
## 899 Viet Nam Sweden [SE] 32025 37679
## 900 Viet Nam Switzerland [CH] 28750 31475
## 901 Viet Nam Taiwan Province of China [TW] 330196 507301
## 902 Viet Nam Thailand [TH] 214645 266984
## 905 Viet Nam United Kingdom [GB] 212798 254841
## 906 Viet Nam United States [US] 368190 552644
## X2017 X2018 X2019 X2020
## 1 131351 134596 168705 32571
## 2 258955 278136 333244 62325
## 3 7581 7338 5884 2034
## 5 8600 9702 10188 2597
## 8 3553 3878 3281 638
## 10 356 420 463 29
## 11 2344 2256 2322 410
## 12 52391 65563 74511 11329
## 14 251 165 312 51
## 17 1403 1318 1381 234
## 18 1868 1827 1764 527
## 21 8691 8635 8925 1750
## 22 22420 27462 33626 6262
## 23 348 246 109 92
## 24 583 491 612 97
## 25 5191 5360 10680 2135
## 26 8705 9125 15767 1939
## 28 144 233 221 46
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## 906 614117 687226 746171 172706
missing_values1 <- sapply(data_impute, function(x) sum(is.na(x)))
print("Missing values in each feature after imputation:")
## [1] "Missing values in each feature after imputation:"
print(missing_values1)
## Destination.Country Origin.Country X2015 X2016
## 0 0 0 0
## X2017 X2018 X2019 X2020
## 0 0 0 0
#Isolate indonesian as the main source destination country.
library(dplyr)
# Filter the data to isolate rows where the 'Destination Country' is 'Indonesia'
indonesia_data1 <- filter(data_impute, `Destination.Country` == "Indonesia")
indonesia_data <- indonesia_data1[!indonesia_data1$Origin.Country %in% c("Total Intra-ASEAN", "Total Country (World)", "Total EU-27"), ]
# Print the filtered data
print(indonesia_data)
## Destination.Country Origin.Country X2015 X2016
## 4 Indonesia Australia [AU] 1099058 1302292
## 5 Indonesia Austria [AT] 22791 24375
## 6 Indonesia Bahrain [BH] 1687 2243
## 7 Indonesia Bangladesh [BD] 16162 39026
## 8 Indonesia Belgium [BE] 38842 43607
## 9 Indonesia Brunei Darussalam [BN] 18304 23693
## 10 Indonesia Canada [CA] 75816 86804
## 11 Indonesia China [CN] 1260700 1556771
## 12 Indonesia Denmark [DK] 28020 36380
## 13 Indonesia Egypt [EG] 13102 19948
## 14 Indonesia Finland [FI] 19029 21031
## 15 Indonesia France [FR] 212575 256229
## 16 Indonesia Germany [DE] 203611 243873
## 17 Indonesia Hong Kong SAR [HK] 94109 101369
## 18 Indonesia India [IN] 319608 422045
## 19 Indonesia Italy [IT] 69427 79424
## 20 Indonesia Japan [JP] 549705 545392
## 21 Indonesia Korea [KR] 387473 386789
## 22 Indonesia Kuwait [KW] 8320 6368
## 23 Indonesia Malaysia [MY] 1458593 1541197
## 24 Indonesia Myanmar [MM] 40635 44721
## 25 Indonesia Netherlands [NL] 175317 200811
## 26 Indonesia New Zealand [NZ] 87923 105393
## 27 Indonesia Norway [NO] 18842 19478
## 28 Indonesia Others Unspecified Countries [O0] 580132 709585
## 29 Indonesia Pakistan [PK] 7976 10098
## 30 Indonesia Philippines [PH] 273630 298910
## 31 Indonesia Portugal [PT] 22206 29286
## 32 Indonesia Qatar [QA] 1608 1856
## 33 Indonesia Russia [RU] 72707 88520
## 34 Indonesia Saudi Arabia [SA] 164778 197681
## 35 Indonesia Singapore [SG] 1624058 1515699
## 36 Indonesia South Africa [ZA] 23052 29229
## 37 Indonesia Spain [ES] 53891 68840
## 38 Indonesia Sri Lanka [LK] 11496 24256
## 39 Indonesia Sweden [SE] 37988 45934
## 40 Indonesia Switzerland [CH] 52309 56700
## 41 Indonesia Taiwan Province of China [TW] 227912 252849
## 42 Indonesia Thailand [TH] 120879 124569
## 43 Indonesia United Arab Emirates [AE] 10325 9016
## 44 Indonesia United Kingdom [GB] 292745 352017
## 45 Indonesia United States [US] 276027 316782
## 46 Indonesia Viet Nam [VN] 50165 60984
## 47 Indonesia Yemen [YE] 8838 9478
## X2017 X2018 X2019 X2020
## 4 1256927 1301478 1386803 256291
## 5 27208 29492 28476 4858
## 6 2457 2324 2631 373
## 7 56503 56564 59777 12866
## 8 48477 50050 46780 5902
## 9 23455 17279 19278 2701
## 10 96139 97908 103616 23200
## 11 2093171 2139161 2072252 239768
## 12 43721 46825 45090 10533
## 13 20345 18075 21354 4337
## 14 24447 27127 22665 6376
## 15 274117 287917 283814 43438
## 16 267823 274166 277653 46361
## 17 98272 91182 50324 2625
## 18 536902 595636 657300 111724
## 19 90022 94288 91229 13260
## 20 573310 530573 519623 92228
## 21 423191 358885 388316 75562
## 22 5760 5551 5762 846
## 23 2121888 2503344 2980753 980118
## 24 48133 28612 46381 12669
## 25 210426 209978 215287 53495
## 26 106914 128366 149010 19947
## 27 22838 24906 23886 5072
## 28 7455 6340 6573 1412
## 29 11424 13448 14663 4110
## 30 308977 217874 260980 50413
## 31 33223 36804 35434 6245
## 32 1859 2104 1989 225
## 33 117532 125728 158943 67491
## 34 182086 165912 157512 31906
## 35 1554119 1768744 1934445 280492
## 36 38073 41962 47657 7350
## 37 81690 85560 83373 11829
## 38 35669 32508 28907 4300
## 39 51417 50381 56402 17600
## 40 61191 60293 57484 8362
## 41 264278 208317 207490 35680
## 42 138235 124153 136699 21303
## 43 8387 7100 9065 1093
## 44 378131 392112 397684 69997
## 45 344766 387856 457832 91782
## 46 77466 75816 96024 19608
## 47 8453 10008 9221 2094
# Alternatively, save the filtered data to a new CSV file
# Reshape the data to a long format
indonesiaData_long <- indonesia_data %>%
pivot_longer(cols = starts_with("X"), names_to = "Year", values_to = "Visitors") %>%
mutate(Year = as.numeric(sub("X", "", Year)))
print(indonesiaData_long)
## # A tibble: 264 × 4
## Destination.Country Origin.Country Year Visitors
## <chr> <chr> <dbl> <dbl>
## 1 Indonesia Australia [AU] 2015 1099058
## 2 Indonesia Australia [AU] 2016 1302292
## 3 Indonesia Australia [AU] 2017 1256927
## 4 Indonesia Australia [AU] 2018 1301478
## 5 Indonesia Australia [AU] 2019 1386803
## 6 Indonesia Australia [AU] 2020 256291
## 7 Indonesia Austria [AT] 2015 22791
## 8 Indonesia Austria [AT] 2016 24375
## 9 Indonesia Austria [AT] 2017 27208
## 10 Indonesia Austria [AT] 2018 29492
## # ℹ 254 more rows
# Count the visitors by origin country
origin_country_counts <- indonesiaData_long %>%
group_by(Origin.Country) %>%
summarize(TotalVisitors = sum(Visitors, na.rm = TRUE)) %>%
arrange(desc(TotalVisitors))
# Print the counts
print(origin_country_counts)
## # A tibble: 44 × 2
## Origin.Country TotalVisitors
## <chr> <dbl>
## 1 Malaysia [MY] 11585893
## 2 China [CN] 9361823
## 3 Singapore [SG] 8677557
## 4 Australia [AU] 6602849
## 5 Japan [JP] 2810831
## 6 India [IN] 2643215
## 7 Korea [KR] 2020216
## 8 United Kingdom [GB] 1882686
## 9 United States [US] 1875045
## 10 Philippines [PH] 1410784
## # ℹ 34 more rows
# Filter to get the top 10 origin countries
top_10_origin_countries <- origin_country_counts %>%
top_n(10, TotalVisitors)
# Filter the long data for these top 10 origin countries
indonesia_data_top10 <- indonesiaData_long %>%
filter(`Origin.Country` %in% top_10_origin_countries$`Origin.Country`)
write.csv(indonesia_data_top10, "cambodia_data.csv", row.names = FALSE)
# univariate
#Histogram Plot: Menampilkan distribusi jumlah pengunjung Indonesia.
# Find the smallest and largest values in the Visitors column
min_visitors <- min(indonesia_data_top10$Visitors)
max_visitors <- max(indonesia_data_top10$Visitors)
# Adjusted Plot with dynamic xbins
histogram_plot <- plot_ly(
data = indonesia_data_top10,
x = ~Visitors,
type = 'histogram',
autobinx = FALSE,
xbins = list(start = min_visitors, end = max_visitors, size = 400000), # Set an appropriate bin size
hoverinfo = 'x+y+text',
text = ~paste("Year:", Year),
marker = list(
color = 'rgba(0, 100, 255, 0.7)',
line = list(color = 'rgba(0, 0, 0, 1)', width = 1)
)
) %>%
layout(
title = list(text = 'Distribution of Total Indonesian Visitors', font = list(size = 24, color = 'darkblue')),
xaxis = list(title = 'Number of Visitors', tickangle = -45, tickfont = list(size = 12, color = 'darkblue')),
yaxis = list(title = 'Count', tickfont = list(size = 12, color = 'darkblue')),
plot_bgcolor = 'rgba(240, 240, 240, 0.9)',
paper_bgcolor = 'rgba(255, 255, 255, 1)',
margin = list(l = 50, r = 50, b = 100, t = 100, pad = 4)
)
# Display the plot
histogram_plot
#multivarit
#Heatmap Plot: Menampilkan hubungan antara tahun dan negara asal pengunjung dengan menggunakan heatmap plot.
heatmap_plot <- plot_ly(data = indonesia_data_top10, x = ~Year, y = ~interaction(Origin.Country, Destination.Country), z = ~Visitors, type = 'heatmap', colorscale = 'Viridis') %>%
layout(
title = list(text = 'Heatmap of Visitors Over Time by Country and Origin', font = list(size = 24, color = 'darkblue')),
xaxis = list(title = 'Year', tickangle = -45, tickfont = list(size = 12, color = 'darkblue')),
yaxis = list(title = 'Country and Origin', tickfont = list(size = 12, color = 'darkblue')),
plot_bgcolor = 'rgba(240, 240, 240, 0.9)',
paper_bgcolor = 'rgba(255, 255, 255, 1)',
margin = list(l = 50, r = 50, b = 100, t = 100, pad = 4)
)
# Display the plot
heatmap_plot
#univariat
# Bar plot using plotly
#Bar Plot: Menampilkan total pengunjung dari top 10 negara asal pengunjung ke Indonesia.
bar_plot <- plot_ly(top_10_origin_countries, x = ~Origin.Country, y = ~TotalVisitors, type = 'bar',
marker = list(color = 'blue')) %>%
layout(title = 'Total Visitors from Top 10 Origin Countries to Indonesia',
xaxis = list(title = 'Origin Country', tickangle = -45),
yaxis = list(title = 'Total Visitors'))
bar_plot
# univariat
# Boxplot Univariat: Menampilkan distribusi jumlah pengunjung Indonesia dengan menggunakan plot boxplot.
box_plot <- plot_ly(indonesia_data_top10,
x = ~`Origin.Country`,
y = ~Visitors,
type = 'box',
boxpoints = 'all',
jitter = 0,
pointpos = 0,
marker = list(color = 'rgba(0, 100, 255, 0.5)'),
line = list(color = 'rgba(255, 0, 0, 0.5)'),
fillcolor = 'rgba(255, 255, 0, 0.5)') %>%
layout(title = 'Distribution of Visitors by Top 10 Origin Countries Over Years',
xaxis = list(title = 'Origin Country', tickangle = -45),
yaxis = list(title = 'Number of Visitors'))
box_plot
#univariat
# Line plot using plotly
#Line Plot: Menampilkan trend jumlah pengunjung Indonesia per tahun untuk top 10 negara asal pengunjung.
line_plot <- plot_ly(indonesia_data_top10, x = ~Year, y = ~Visitors, color = ~`Origin.Country`,
type = 'scatter', mode = 'lines+markers') %>%
layout(title = 'Trend of Indonesia Visitors Over Years by Top 10 Origin Countries',
xaxis = list(title = 'Year'),
yaxis = list(title = 'Number of Visitors'))
line_plot
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
#univariat
# Grafik violin plot
#Violin Plot: Menampilkan distribusi jumlah pengunjung Indonesia dengan memperlihatkan kepadatan dan bentuk distribusi.
violin_plot <- plot_ly(indonesia_data_top10, x = ~Origin.Country, y = ~Visitors, type = 'violin') %>%
layout(title = 'Violin Plot of Visitors by Origin Country',
xaxis = list(title = 'Origin Country'),
yaxis = list(title = 'Number of Visitors'))
violin_plot
#univariat (iseng aja)
# Word Cloud: Menampilkan top 10 negara asal pengunjung dengan menggunakan word cloud.
# Membuat plot untuk top 10 negara asal pengunjung menggunakan word cloud
library(wordcloud)
## Warning: package 'wordcloud' was built under R version 4.3.3
## Loading required package: RColorBrewer
top_10_origin_countries_wordcloud <- indonesia_data_top10 %>%
group_by(Origin.Country) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(desc(TotalVisitors)) %>%
top_n(10, TotalVisitors) %>%
mutate(Origin.Country = as.character(Origin.Country))
wordcloud(top_10_origin_countries_wordcloud$Origin.Country, top_10_origin_countries_wordcloud$TotalVisitors, min.freq = 1, max.words = 10, random.order = FALSE, rot.per = 0.5, use.r.layout = FALSE, main = "Top 10 Origin Countries of Visitors to Indonesia")
## Warning in wordcloud(top_10_origin_countries_wordcloud$Origin.Country,
## top_10_origin_countries_wordcloud$TotalVisitors, : Australia [AU] could not be
## fit on page. It will not be plotted.
# Membuat daftar top 10 negara tujuan yang paling disukai oleh Indonesia
top_10_destination_countries <- indonesia_data_top10 %>%
group_by(Destination.Country) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(desc(TotalVisitors)) %>%
top_n(10, TotalVisitors) %>%
pull(Destination.Country)
# Memfilter data panjang untuk top 10 negara tujuan yang paling disukai oleh Indonesia
filtered_data <- indonesia_data_top10 %>%
filter(Destination.Country %in% top_10_destination_countries)
# Menampilkan data yang telah difilter
filtered_data
## # A tibble: 60 × 4
## Destination.Country Origin.Country Year Visitors
## <chr> <chr> <dbl> <dbl>
## 1 Indonesia Australia [AU] 2015 1099058
## 2 Indonesia Australia [AU] 2016 1302292
## 3 Indonesia Australia [AU] 2017 1256927
## 4 Indonesia Australia [AU] 2018 1301478
## 5 Indonesia Australia [AU] 2019 1386803
## 6 Indonesia Australia [AU] 2020 256291
## 7 Indonesia China [CN] 2015 1260700
## 8 Indonesia China [CN] 2016 1556771
## 9 Indonesia China [CN] 2017 2093171
## 10 Indonesia China [CN] 2018 2139161
## # ℹ 50 more rows
library(plotly)
scatter_matrix_plot <- plot_ly(indonesia_data_top10,
type = 'splom',
dimensions = list(
list(label = 'Visitors',
values = ~Visitors),
list(label = 'Year',
values = ~Year),
list(label = 'Origin Country',
values = ~Origin.Country)
),
marker = list(showscale = FALSE,
opacity = 0.7,
size = 5,
color = ~Origin.Country,
colorscale = 'Viridis')) %>%
layout(title = 'Scatter Matrix Plot of Top 10 Origin Countries')
scatter_matrix_plot
#matriks scatter plot yang menunjukkan hubungan antara variabel Pengunjung, Tahun, dan Negara Asal pada dataset indonesia_data_top10. Plot dapat membantu mengidentifikasi tren atau pola apa pun dalam data, serta korelasi antar variabel.
library(plotly)
scatter_plot_matrix <- plot_ly(indonesia_data_top10, x = ~Visitors, y = ~Year, color = ~Origin.Country, type = 'scatter', mode = 'markers') %>%
layout(title = 'Scatter Plot Matrix of Visitors, Year, and Origin Country',
xaxis = list(title = 'Visitors'),
yaxis = list(title = 'Year'))
scatter_plot_matrix
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
#top 3 visitors to Indonesia with percentages and creates a bar plot to show the results. The plot displays the origin country on the x-axis, the total visitors on the y-axis, and the percentage of visitors from each country as a label on the bar.
# Calculate the top 3 visitors to Indonesia with percentages
top_3_visitors <- indonesia_data_top10 %>%
group_by(Origin.Country) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(desc(TotalVisitors)) %>%
top_n(3, TotalVisitors) %>%
mutate(Percentage = (TotalVisitors / sum(TotalVisitors)) * 100)
# Create a bar plot to show the top 3 visitors to Indonesia with percentages
# Calculate the top 3 years with the most visitors to Indonesia
top_3_years <- indonesia_data_top10 %>%
group_by(Year) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(desc(TotalVisitors)) %>%
top_n(3, TotalVisitors)
# Calculate the percentage of each year
top_3_years$Percentage <- (top_3_years$TotalVisitors / sum(indonesia_data_top10$Visitors)) * 100
# Create a bar plot to show the top 3 years with the most visitors to Indonesia
library(ggplot2)
# Calculate the top 3 years with the least visitors to Indonesia
top_3_years <- indonesia_data_top10 %>%
group_by(Year) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(TotalVisitors) %>%
top_n(3, TotalVisitors)
# Calculate the top 3 years with the least visitors to Indonesia
top_3_least_years <- indonesia_data_top10 %>%
group_by(Year) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(TotalVisitors) %>%
top_n(3, TotalVisitors)
# Create a bar plot to show the top 3 years with the least visitors to Indonesia
# bar plot that shows the top 5 origin countries with the least visitors to Indonesia.
library(plotly)
top_5_least_origin_countries <- indonesia_data_top10 %>%
group_by(Origin.Country) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(TotalVisitors) %>%
top_n(5, TotalVisitors)
bar_plot <- plot_ly(top_5_least_origin_countries, x = ~Origin.Country, y = ~TotalVisitors, type = 'bar',
marker = list(color = 'blue')) %>%
layout(title = 'Top 5 Origin Countries with the Least Visitors to Indonesia',
xaxis = list(title = 'Origin Country'),
yaxis = list(title = 'Total Visitors'))
bar_plot
#bar plot that shows the top 5 origin countries with the least visitors to Indonesia.
library(plotly)
top_3_least_years <- indonesia_data_top10 %>%
group_by(Year) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(TotalVisitors) %>%
top_n(3, TotalVisitors)
bar_plot <- plot_ly(top_3_least_years, x = ~Year, y = ~TotalVisitors, type = 'bar',
marker = list(color = 'blue')) %>%
layout(title = 'Top 3 Years with the Least Visitors to Indonesia',
xaxis = list(title = 'Year'),
yaxis = list(title = 'Total Visitors'))
bar_plot
# Process the data to get the top 10 origin countries and their percentages
top_10_origin_countries <- indonesia_data_top10 %>%
group_by(Origin.Country) %>%
summarise(TotalVisitors = sum(Visitors)) %>%
arrange(desc(TotalVisitors)) %>%
top_n(10, TotalVisitors) %>%
mutate(Percentage = (TotalVisitors / sum(TotalVisitors)) * 100)
# Create the pie chart using Plotly
pie_chart <- plot_ly(
data = top_10_origin_countries,
labels = ~Origin.Country,
values = ~Percentage,
type = 'pie',
textinfo = 'label+percent',
insidetextorientation = 'radial'
) %>%
layout(
title = 'Percentage of Visitors from Top 10 Origin Countries to Indonesia',
showlegend = TRUE
)
# Display the plot
pie_chart
#Explanation: This pie chart displays the percentage distribution of visitors from the top 10 origin countries. It provides a quick view of the major sources of visitors.
# Data preparation and processing
data_impute2 <- data_impute[!data_impute$Origin.Country %in% c("Total Intra-ASEAN", "Total Country (World)", "Total EU-27"), ]
data_impute3 <- separate(data_impute2, Origin.Country, into = c("Origin.Country", "Country.Code"), sep = "\\[|\\]")
## Warning: Expected 2 pieces. Additional pieces discarded in 455 rows [1, 2, 3, 4, 5, 6,
## 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].
missing_values2 <- sapply(data_impute3, function(x) sum(is.na(x)))
print("Missing values in each feature:")
## [1] "Missing values in each feature:"
print(missing_values2)
## Destination.Country Origin.Country Country.Code X2015
## 0 0 0 0
## X2016 X2017 X2018 X2019
## 0 0 0 0
## X2020
## 0
library(plotly)
library(rjson)
library(dplyr)
library(countrycode)
## Warning: package 'countrycode' was built under R version 4.3.3
# Convert Country.Code to ISO3
data_impute3 <- data_impute3 %>%
mutate(ISO3 = countrycode(Country.Code, "iso2c", "iso3c"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ISO3 = countrycode(Country.Code, "iso2c", "iso3c")`.
## Caused by warning:
## ! Some values were not matched unambiguously: O0, ZB
# Check for any NA values in the ISO3 column
missing_iso3 <- data_impute3 %>% filter(is.na(ISO3))
print(missing_iso3)
## Destination.Country Origin.Country Country.Code X2015
## 1 Brunei Darussalam Others Unspecified Countries O0 3435
## 2 Cambodia Others Unspecified Countries O0 18037
## 3 Cambodia Serbia and Montenegro ZB 863
## 4 Indonesia Others Unspecified Countries O0 580132
## 5 Lao PDR Others Unspecified Countries O0 37524
## 6 Malaysia Others Unspecified Countries O0 320900
## 7 Philippines Others Unspecified Countries O0 271037
## 8 Singapore Others Unspecified Countries O0 278200
## 9 Thailand Others Unspecified Countries O0 1124238
## 10 Viet Nam Others Unspecified Countries O0 299075
## X2016 X2017 X2018 X2019 X2020 ISO3
## 1 3273 3854 4113 9581 825 <NA>
## 2 65755 39245 18788 6006 1120 <NA>
## 3 1052 1191 1249 1202 523 <NA>
## 4 709585 7455 6340 6573 1412 <NA>
## 5 43197 49211 34650 37412 48353 <NA>
## 6 1223374 130433 420230 440935 50108 <NA>
## 7 257991 210534 154380 171283 114314 <NA>
## 8 395117 438535 479029 636449 131649 <NA>
## 9 1308314 1386184 1406413 1418321 350954 <NA>
## 10 544458 256166 381612 514324 190276 <NA>
data_impute4 <- na.omit(data_impute3)
data_aggregated <- data_impute4 %>%
mutate(Total_Visitors = X2015 + X2016 + X2017 + X2018 + X2019 + X2020) %>%
select(Destination.Country, Origin.Country, ISO3, Total_Visitors)
# Create the interactive choropleth map
map <- plot_ly(data = data_aggregated,
type = 'choropleth',
locations = ~ISO3,
z = ~Total_Visitors,
text = ~paste("Origin Country:", Origin.Country, "<br>",
"Total Visitors:", Total_Visitors),
colorscale = "Viridis",
colorbar = list(title = "Total Visitors"))
# Configure the layout
map <- map %>%
layout(title = 'Total Visitors by Origin Country to Destination Country (2015-2020)',
geo = list(showframe = FALSE,
showcoastlines = FALSE,
projection = list(type = 'equirectangular')))
# Display the map
map
write.csv(data_impute4, "data.csv", row.names = FALSE)